We present an efficient method for facial recognition using hyperspectral imaging and orthogonal subspaces. Projecting
the data into orthogonal subspaces has the advantage of compactness and reduction of redundancy. We focus on two
approaches: Principal Component Analysis and Orthogonal Subspace Projection. Our work is separated in three stages.
First, we designed an experimental setup that allowed us to create a hyperspectral image database of 17 subjects under
different facial expressions and viewing angles. Second, we investigated approaches to employ spectral information for
the generation of fused grayscale images. Third, we designed and tested a recognition system based on the methods
described above. The experimental results show that spectral fusion leads to improvement of recognition accuracy when
compared to regular imaging. The work expands on previous band extraction research and has the distinct advantage of
being one of the first that combines spatial information (i.e. face characteristics) with spectral information. In addition,
the techniques are general enough to accommodate differences in skin spectra.
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